Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data by Jie Wang, Huazhu Xue, Guotao Dong, Qian Yuan, Ruirui Zhang, Runsheng Jing

“…In Wudaoliang, the R value increases from 0.54 to 0.70. The RMSE value is 0.03 (cm³/cm³). …”
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Real-time monitoring of soil moisture in cotton fields using electromagnetic induction technology by Yang Gao, Quanze Hu, Jiaojiao Hui, Lin Chang, Mei Zeng, Qingsong Jiang

“…The ECa in vertical and horizontal modes was collected by EM38-MK2, and the optimal linear modeling factor was determined by combining the θg values at different soil depths. …”
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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction by Xinyue Lv, Youli Li, Lili Zhangzhong, Chaoyang Tong, Yibo Wei, Guangwei Li, Yingru Yang

“…The Penman-Monteith model, endorsed by the Food and Agriculture Organization of the United Nations (FAO), is currently the predominant approach for estimating crop water needs. …”
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Analysis of the spatiotemporal dynamics of grassland carbon sinks in Xinjiang via the improved CASA model by Xuewei Liu, Renping Zhang, Jing Guo, Haoen Xu, Yuhao Miao, Feifei Niu, Zhengjie Gao, Xiaming Yang, Fengqin Xiong, Jianli Zhang

“…The analysis revealed that (1) the R2 value of the optimal CASA model was 0.70, and the RMSE was 22.30 gC·m−2. (2) The 23-year average NEP in Xinjiang grassland was 113.00 gC·m−2·a−1, with 82.20 % carbon sink areas and 17.80 % carbon source areas. (3) NEPs of meadows, steppes, and desert grasslands in the Xinjiang region tended to increase during spring, summer, and autumn. (4) During the 23-year period, 15.77 % of the Xinjiang grassland area transitioned from carbon sources to carbon sinks. (5) Relative humidity and soil pH strongly influenced the NEP of Xinjiang grassland. …”
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Slope deformation prediction based on GA–BP neural networks by Wenhui TAN, Kai LI, Huimin LIU, Meifeng CAI, Qifeng GUO

“…We evaluated their prediction effectiveness by examining the RMSE of predicted values, the number of training operations, and model adaptability to training and validation sets. …”
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Water Level Variation Monitoring in East Lake, Wuhan Based on Satellite Altimetry by LIU Huo-sheng, WANG Hai-hong, YU Qian-hui, LU Liang, QIN Peng-cheng, LIU Yi-bing

“…[Results] (1) Statistical analysis of pulse peakiness and waveform width from the lake surface altimetry echoes revealed that approximately 50% of East Lake’s waveforms exhibited specular reflections with distinct sharp peaks, while about 30% displayed complex shapes containing two or more peaks. (2) The results of accuracy validation using the on-site measured data of water levels showed that the 50% threshold retracking method achieved optimal performance, with a root mean square error (RMSE) of 0.108 m and a correlation coefficient of 0.87. (3) Based on the 50% threshold retracking method, and using Jason-3 data, the water level time series of East Lake from September 2017 to February 2022 was established. …”
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